A guide for studying among-individual behavioral variation from movement data in the wild

Covetable Corpses and Plastic Beetles—The Socioecological Behavior of Burying Beetles

Animal personality

Animals can differ in their individual movement behaviors during their daily displacements. Studies of animal movement patterns often disregard the need to understand individual variation in these patterns and the role of this variation in shaping population distributions of movements. To assess the link between individual movements and population distributions, we radio-tracked 13 Fowler’s Toads (Anaxyrus fowleri) in Long Point, Ontario, Canada, after their breeding season. We recorded individual coordinates every 30 min, from the moment they emerged from their burrows around dusk until they burrowed in the morning, and obtained a total of 157 movements. We used the resulting series of step lengths and turning angles in a Hidden Markov Model to estimate movement type as “directed” or “non-directed.” We also assigned to each encounter one of 3 space-dependent movement states: “foraging”, “in transit”, or “searching”, and found that movement type and state switched within individuals irrespective of time during a single night. We find that switching between movement types varied throughout the night, leading to stochastic within-individual variation in movements, and the distribution of movement distances differed significantly among individuals. Movement states, however, were time-dependent, suggesting displacement routines were similar across individuals. Variation in movement behavior within individuals scales up to result in variation among individuals which, in turn, was found to shape the distribution of the sampled population. Our findings therefore underline the importance of a multi-scale approach in the study of movement.

There is no doubt that interest in both ultimate and proximate causes of individual variation is increasing. In a recent paper, Wolf et al. [2007] demonstrate how lifehistory theory can be used to explain the continued presence of divergent phenotypes, or personalities. Regarding the question of limited plasticity, an impetus of the 2006 Karger Workshop was to point out that behavioral states have physiological correlates, and physiology may control behavior as much as behavior controls physiology. In some cases, neurobiological mechanisms that cause variation in behavior are quite plastic and modified by experience, in other cases certain traits appear to be life-long characteristics. This symposium’s keynote lecturer, Jaap Koolhaas, and co-authors [Koolhaas et al., 1999] were among the first to point out how genetic and epigenetic factors combine to generate a relatively stable trait characteristic that confers differential adaptation to environmental conditions such as population density, social stability and food availability. The aim of the symposium was to synthesize current knowledge of proximate and ultimate mechanisms involved in maintaining individual variation in behavior, focusing on the relationship between brain structural plasticity, cognitive processes, and behavioral outcome in comparative and mammalian models. During the symposium, we were shown several examples of both heritable and apparently consistent trait associations [e.g. Veenema and Neumann, 2007; Schjolden and Winberg, 2007], and rapidly modifiable responses [Burmeister, The concept that humans show consistent individual differences in personality is familiar to us all, and most people would agree with the definition suggested by Pervin and John [1997], who stated that personality is: ‘Those characteristics of individuals that describe and account for consistent patterns of feeling, thinking, and behaving’. In other words: If what a man does in a certain situation predicts his behavior the next time he is in a similar situation, or even his behavior in other contexts, he has a personality. Behavioral correlation across situations, i.e. the analogue of personality, have recently been documented by ethologists and behavioral ecologists in a broad range of species, including several mammals, birds, lizards, amphibians, fish, mollusks, and arthropods [see e.g. Gosling, 2001; Bell and Stamps, 2004; Sinn and Moltscha niwskyj, 2005]. Referring to the fact that suites of correlated traits are often described as syndromes, Sih et al. [2004] coined the term behavioral syndromes, and pointed out that this phenomenon can have important ecological and evolutionary implications. For instance, the notion of behavioral syndromes implies limited behavioral plasticity. Limited plasticity, in turn, may pose problem in rapidly changing environments, such as humandisturbed habitats [Sol and Lefebvre, 2000; Sol et al., 2002]. Another question is how variation is maintained in a population. In other words, why does a population not simply drift towards a homogeneous group of the most successful phenotypes? Published online: September 18, 2007

Conservation Behavior Is Here to Stay

Behavioral syndromes and animal personalities are an increasingly active area of research within behavioral ecology (Bell 2007), and this interest stems in part from their potential evolutionary implications. In other areas of evolutionary ecology, trait covariances—like those observed in behavioral syndromes—have been demonstrated to have profound evolutionary implications (Lande and Arnold 1983). Trait covariances can dramatically affect how populations respond to selection by potentially constraining responses, generating trade-offs, or otherwise shaping evolutionary trajectories (Blows and Hoffmann 2005; Roff and Fairbairn 2007; Walsh 2007). Via these same evolutionary responses, behavioral syndrome research may well have similarly profound ramifications for our understanding of behavioral evolution. However, as with many novel areas of research, methodologies for the study of behavioral syndromes are still developing (e.g., Dingemanse et al. 2010). While examining behavioral syndrome structure in hissing cockroaches (Gromphadorhina portentosa), Logue et al. (2009) discussed 2 key methodological issues for the study of behavioral syndromes: 1) How the order in which behaviors are assayed can affect subsequent behavioral responses and 2) How the evaluation of numerous behaviors results in a risk that correlations are statistical artifacts. Both of these aspects are of importance for the study of behavioral syndromes and warrant additional discussion as they may influence the inferences that researchers draw. Here, I focus on extending the discussion of order effects, their implications to behavioral syndrome research, and how to methodologically treat them. I also expand on some of the points made by Logue et al. (2009) regarding the evaluation of numerous behavioral correlations and discuss the role of confirmatory analyses.

The variability in motion behaviour properties was investigated for three species of intertidal gastropods at the inter-specific, inter-individual and individual levels in the absence of abiotic and biotic cues. Interspecific differences in movement patterns were reminiscent of the optimal searching behaviours expected for Austrocochlea porcata, Nerita atramentosa and Bembicium melanostomum in their natural environment. Specifically, N. atramentosa, A. porcata and B. melanostomum respectively displayed extensive and intensive foraging strategies consistent with their feeding ecology. The related inter-individual variability within each species highlights the potential ability of species to adapt their movement patterns to new environmental conditions and to persist over long-term changes. Finally, the strong variability observed in the speed and turning angle of individuals of the three species and the resulting behavioural plasticity may be an adaptive strategy to optimize energy expenditure and to react to an environmental fluctuation. Specifically, it is suggested that the lack of significant differences in individual behavioural variability between the three species indicates that despite clear inter-specific differences in motion behaviour, at the individual level A. porcata, N. atramentosa and B. melanostomum have similar abilities to face environmental fluctuations. This work stresses that individual variability in the motion behaviour of intertidal gastropods constitutes a fundamental evolutionary advantage when facing heterogeneous environmental conditions.

In recent years, considerable computational advancements have been made allowing automated analysis of behavioural endpoints using video cameras. However, the results of such analyses are often confounded by a large variation among individuals, making it problematic to derive endpoints that allow distinguishing treatment effects in behavioural studies. In this study, we quantitatively analysed the effects of light conditions on the swimming behaviour of the freshwater amphipod Gammarus pulex by high-throughput tracking, and attempted to unravel among individual variation using size and sex. For this, we developed the R-package Kinematics, allowing for the rapid and reproducible analysis of the swimming behaviour (speed, acceleration, thigmotaxis, curvature and startle response) of G. pulex, as well as any other organism. Our results show a considerable amount of variation among individuals (standard deviation ranging between 5 and 115 % of the average swimming behaviour). The factors size and sex and the interaction between the two only explained a minor part of this found variation. Additionally, our study is the first to quantify the startle response in G. pulex after the light is switched on, and study the variability of this response between individuals. To analyse this startle response, we established two metrics: 1) startle response magnitude (the drop in swimming velocity directly after the light switches on), and 2) startle response duration (the time it takes to recover from the drop in swimming velocity to average swimming speed). Almost 80 % of the individuals showed a clear startle response and, therefore, these metrics demonstrate a great potential for usage in behavioural studies. The findings of this study are important for the development of appropriate experimental set-ups for behavioural experiments with G. pulex.

According to recent studies on animal personalities, the level of behavioral plasticity, which can be viewed as the slope of the behavioral reaction norm, varies among individuals, populations, and species. Still, it is conceptually unclear how the interaction between environmental variation and variation in animal cognition affect the evolution of behavioral plasticity and expression of animal personalities. Here, we (1) use literature to review how environmental variation and individual variation in cognition explain population and individual level expression of behavioral plasticity and (2) draw together empirically yet nontested, conceptual framework to clarify how these factors affect the evolution and expression of individually consistent behavior in nature. The framework is based on simple principles: first, information acquisition requires cognition that is inherently costly to build and maintain. Second, individual differences in animal cognition affect the differences in behavioral flexibility, i.e. the variance around the mean of the behavioral reaction norm, which defines plasticity. Third, along the lines of the evolution of cognition, we predict that environments with moderate variation favor behavioral flexibility. This occurs since in those environments costs of cognition are covered by being able to recognize and use information effectively. Similarly, nonflexible, stereotypic behaviors may be favored in environments that are either invariable or highly variable, since in those environments cognition does not give any benefits to cover the costs or cognition is not able to keep up with environmental change, respectively. If behavioral plasticity develops in response to increasing environmental variability, plasticity should dominate in environments that are moderately variable, and expression of animal personalities and behavioral syndromes may differ between environments. We give suggestions how to test our hypothesis and propose improvements to current behavioral testing protocols in the field of animal personality.

Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two‐dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an r package, which provides tools to animate movement trajectories, overlaying simultaneous uni‐ or multi‐temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame‐wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates.

Statistical modelling of animal movement data is a rapidly growing area of research. Typically though, these models have been developed for analysing the tracks of individual animals and we lose sight of the impact animals have on each other with regards to their movement behaviours. We aim to develop a model with a flexible social framework that allows us to capture that information. Our approach is based on the concept of social hierarchies, and this is embedded in a multivariate diffusion process which models the movement of a group of animals. The possibility of switching between behavioural states facilitates dynamic social behaviours and we augment the observed data with sampled state switching times in order to model the animals' behaviour naturally in continuous time. In addition, this enables us to carry out exact inference in a Bayesian setting with the benefits of being able to handle regular, irregular and missing data. All movement and behaviour parameters are estimated with Markov chain Monte Carlo methods. We examine the capability of our model with simulated data before fitting it to GPS locations of five wild olive baboons Papio anubis. The results enable us to identify which animals are influencing the movement of others and when, which provides both a dynamic and long‐term static insight into the group's social behaviours. Our model offers a flexible method in continuous time with which to model the network of social interactions within animal movement. Doing so avoids the limitations caused by a discrete‐time approach and it allows us to capture rich information with regards to a group's social structure, leading to constructive applications in conservation and management decisions. However, currently it is a computationally expensive task to fit the model to data, which in turns limits extending the model to more fruitful but complex cases such as heterogeneity in space or individual characteristics. Furthermore, our social hierarchy approach assumes all relevant animals are tracked and that any interactions have some ordering, both of which narrow the scope within which this approach is appropriate.

Ectothermic organisms respond to rapid environmental change through a combination of behavioral and physiological adjustments. As behavioral and physiological traits are often functionally linked, an effective ectotherm response to environmental perturbation will depend on the direction and magnitude of their association. The role of various modifiers in behavioral-physiological relationships remains largely unexplored. We applied a repeated-measures approach to examine the influence of body temperature and individual variation on the link between resting metabolic rate (RMR) and exploratory locomotor activity (ELA) in juvenile Alpine newts, Ichthyosaura alpestris. We analyzed trait relationships at two body temperatures separately and as parameters, intercepts and slopes, of thermal reaction norms for both traits. Body temperature affected the level of detectable among-individual variation in two different directions. Among-individual variation in ELA was detected at 12°C, while RMR was repeatable at 22°C. We found no support for a link between RMR and ELA at either temperature. While analysis of intercepts revealed among-individual variation in both traits, among-individual variation in slopes was detected in RMR only. Intercepts were positively associated at the individual, but not the whole-phenotypic, level. For ELA, the target of selection should be individual trait values across temperatures, rather than their thermal sensitivities. The positive association between intercepts of thermal reaction norms for ELA and RMR suggests that phenotypic selection acts on both traits in a correlated fashion. Measurements at one body temperature and within-individual variation hide the metabolic-behavioral relations. We conclude that correlative studies on flexible behavioral and physiological traits in ectotherms require repeated measurement at two or more body temperatures in order to avoid misleading results. This approach is needed to fully understand ectotherm responses to environmental change and its impact on their population dynamics.

Assessment of usual dietary intake of a population is essential to understand the nutrition status of the population. Methods like 24-hour dietary recall and food records are commonly used for this task. However, within-individual variation exists in the food record data. Methods considering measurement errors have been used to evaluate the association between nutrient intakes and diseases. Few have been devoted to estimating the distribution of usual daily intakes. This paper proposes applying the formulation of the overdispersed exponential family to estimate the distribution of usual nutrient intake and applies the adjustments developed by Liu to reduce the variance of the distribution. The proposed method has the advantages of working on the original scale of data and implementation convenience. The adjustment of the variance is carried out by dividing the variance into within-individual variance and among-individual variance. The adjusted variance, then, is used to estimate the distribution of the usual daily intake. Sampling weights are considered in all the steps, except the estimation of the ratio of within to among variance. The data for this study are from the Nutritional and Health Survey in Taiwan conducted between 1993 and 1996 (NAHSIT, 1993-1996). An independent external set of data for people aged between 18 and 28 years (49 males and 20 females) is used to estimate the ratio of within to among individual variance. Due to the availability of data in estimating the ratio of within to among individual variance, 430 males and 422 females in the NAHSIT sample are used to estimate the distribution of usual daily intakes for people aged between 18 and 28 in Taiwan. The distribution also allows us to estimate the proportion of people who do not meet the recommended daily nutrient allowance (RDNA). The results show that the intakes of calcium and vitamin E of this group of people fall far below RDNA.

Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions

Animal individuals show patterns of behavior that are stable within individuals but different among individuals. Such individual differences are potentially associated with differences in foraging efficiency and in fitness. Furthermore, behavioral responses may be correlated in specific suites of so-called behavioral syndromes that are consistent across different contexts and with time. Here, we present a field investigation on individual differences between wild, free-flying nectarivorous bats (Glossophaga commissarisi) in the foraging context. We further investigated how individual differences affect choice performance, and we examined their interdependence within hypothesized behavioral syndrome structures. Free-ranging bats were individually identified as they visited an array of 24 artificial flowers with nectar of high or low sugar concentration. We found that three behavioral measures of foraging behavior were individually stable over the two-month observation period. We investigated the link between individual behavioral measures and measures of choice performance using generalized linear mixed models. Individual measures of choice performance showed significant repeatability, and we found evidence that bats making more visits per bout tend to be slower in learning to avoid unprofitable flowers. We used a multi-response generalized linear mixed model to estimate between-individual correlations and compare hypothesized syndrome structures. There were no clear patterns of between-individual correlations among the behavioral measures in our study, despite the measures exhibiting significant repeatability. This may indicate that foraging behavior depends on multiple individual behavior dimensions that are not adequately described by simple models of behavioral syndromes. Nectar-feeding bats, like other animals including humans, have their own peculiar ways of consuming food that differ among individuals of the same species. We characterized the feeding habits of individuals in a population of wild, free-flying bats that were trained to gather nectar from computer-automated artificial flowers in a Costa Rican rainforest. Individual bats responded to the experimental conditions in different ways, but consistently over the two-month observation period. For example, some bats frequently returned to the same flowers, while others tended to meticulously probe most flowers they encountered before returning to a previously visited location. Interestingly, bats also consistently differed in how fast they learned to avoid flowers with dilute nectar and the faster learners were the bats that made only a few visits on each feeding trip. This suggests that individual foraging strategies might be associated with differences in foraging efficiency.